Abstract

Accurate plasma shape reconstruction is crucial for the stable operation of tokamak devices. In this study, a plasma optical boundary reconstruction system is constructed on the Experimental Advanced Superconducting Tokamak (EAST), and real-time shape reconstruction based on an optical method is realized for the first time on EAST. First, a boundary extraction algorithm based on gray features is designed to extract the optical boundary from plasma optical images. Then, to reconstruct the optical boundary in the tokamak coordinate system, a camera calibration algorithm is developed based on feature point matching, and a coordinate mapping algorithm is designed based on plasma geometric features. Then, the system reconstructs the plasma boundary shape based on images acquired by a camera. Finally, the system is deployed in real-time on EAST, and its reconstruction rate meets the requirement of the EAST plasma shape control system. This study validates the feasibility of using an optical boundary shape reconstruction method on a tokamak device for real-time plasma shape reconstruction, providing a new shape reconstruction approach during steady-state discharge in future fusion devices.

• Book : 65(1)
• Pub. Date : 2025
• Page : pp.016027
• Keyword :

Abstract

By employing both nonlinear gyrokinetic simulation and analytical theory, we have investigated the effects of zonal (electromagnetic) fields on the energetic particle’s (EPs) drive of reversed-shear Alfvén eigenmodes (AEs) in tokamak plasmas. Contrary to the conventional expectation, simulations with zonal fields that are turned on and off in the EP dynamics while keeping the full nonlinear dynamics of the thermal plasma indicate that zonal fields further enhance the instability drive and thus lead to a higher saturation level. These puzzling simulation results can be understood analytically in terms of the general fishbone-like dispersion relation with the correspondingly different EP phase-space structures induced by the zonal fields. Analytical expressions for the zonal fields that are beat driven by the reversed-shear AEs are also derived, and shown to be in good agreement with the simulation results.

• Book : 65(1)
• Pub. Date : 2025
• Page : pp.016018
• Keyword :

• Book : 1010()
• Pub. Date : 2025
• Page : pp.116752
• Keyword :

• Book : 31(1)
• Pub. Date : 2025
• Page : pp.187-193
• Keyword :

Abstract

The advent of machine learning (ML) has revolutionized the research of plasma confinement, offering new avenues for exploration. It enables the construction of models that effectively streamline the simulation process. While previous first-principles simulations have provided physics-based transport information, they have been inadequate fast for real-time applications or plasma control. In order to address this challenge, we introduce SExFC, a surrogate model based on the Gyro-Landau Extended Fluid Code (ExFC). An approach of physics-based database construction is detailed, as well the validity is illustrated. Through harnessing the power of ML, SExFC offers the capability to deliver rapid and precise predictions, facilitating real-time applications and enhancing plasma control. The proposed model integrates the recurrent neural network (RNN) algorithm, specifically leveraging the Gated Recurrent Unit (GRU) for iterative prediction of flux evolutions based on radial profiles. Therefore, the SExFC model has the potential to enable rapid and physics-based predictions that can be seamlessly integrated into future real-time plasma control systems.

• Book : 65(1)
• Pub. Date : 2025
• Page : pp.016015
• Keyword :

• Book : 25(1)
• Pub. Date : 2025
• Page : pp.04024310
• Keyword :

• Book : 227()
• Pub. Date : 2025
• Page : pp.112378
• Keyword :

• Book : 227()
• Pub. Date : 2025
• Page : pp.112364
• Keyword :

• Book : 1070(p1)
• Pub. Date : 2025
• Page : pp.170035
• Keyword :

• Book : 1070(p2)
• Pub. Date : 2025
• Page : pp.170005
• Keyword :